CN110414819B - Work order scoring method - Google Patents
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- CN110414819B CN110414819B CN201910656766.8A CN201910656766A CN110414819B CN 110414819 B CN110414819 B CN 110414819B CN 201910656766 A CN201910656766 A CN 201910656766A CN 110414819 B CN110414819 B CN 110414819B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention discloses a worksheet scoring method, which belongs to the technical field of artificial intelligence and comprises the following steps: in the training process of the learning model, in the step A1, a label value is set for each preset statement; step A2, word segmentation processing is carried out on the preset sentences; step A3, vectorizing the first words respectively; step A4, generating a learning model; the method comprises the steps of (A1) grading work order feedback data, and (B1) word segmentation processing of work order feedback sentences; step B2, carrying out vectorization processing on each second word; step B3, inputting the second processing result into a learning model to obtain a label value; step B4, generating a first score according to the label value; the beneficial effects of the technical scheme are as follows: the problems of low accuracy and low recognition rate in the traditional programming are effectively solved, the problem that the traditional programming method is difficult to effectively understand meaning of variable sentences input manually is solved, the accuracy is greatly improved, and objectivity and effectiveness of scoring the service quality of the work order in the examination are ensured.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a worksheet scoring method.
Background
With the continuous development of novel technical means such as machine learning, big data analysis and the like, artificial intelligence is rapidly integrated into society and human life, and the improvement of the efficiency of the existing work performance assessment through the artificial intelligence technology becomes realistic. In the prior art, aiming at the working mode of electronic work order dispatching and user feedback processing, the service quality and service quantity scoring process is carried out on maintenance personnel of the electronic work order, the feedback data of the served work order is firstly fetched by core post personnel of the profession in an enterprise, then subjective scoring is carried out on the maintenance personnel serving the work order according to the feedback data of the user in each work order, finally the feedback quantity in the feedback data of each staff is counted to determine the workload of the staff, the manual work order processing quality and processing quantity judgment consumes a great deal of time and energy, the workload of the staff of the core post is greatly increased, and meanwhile, the defect of low judgment efficiency and different subjective judgment standards is overcome, so that the intelligent analysis is carried out on each sentence fed back in the work order by utilizing the program, and a new exploration way is formed.
However, in the conventional programming method, the general meaning of the feedback statement in the work order is often judged by judging whether the keyword appears in the sentence, and because the keyword is preset in advance when the program is programmed, the word expressing the key information of the feedback statement often appears in the keyword, so that the meaning of the statement cannot be accurately understood and effectively identified by the program, the conventional programming method is difficult to effectively understand the meaning of the variable statement input by the human, thereby causing the high scoring error rate of the program on the processing quality of the work order and losing the objectivity of performance assessment evaluation.
Disclosure of Invention
According to the problems in the prior art, a work order scoring method is provided, machine learning and artificial intelligence technology is introduced in the method, namely, a corresponding learning model is automatically generated by a program according to a preset algorithm in a mode of inputting a learning set in advance, then semantic recognition is carried out on feedback sentences in feedback data input subsequently by using the learning model, and intelligent scoring is carried out according to recognition results, so that the problems of low accuracy and low recognition rate caused by searching sentence semantics through preset keywords in traditional programming are effectively solved, the problem that the traditional programming method is difficult to effectively understand meaning of manually input variable sentences is solved, the accuracy of analyzing the feedback sentences in the work order by using the program is greatly improved, and objectivity and effectiveness of scoring the service quality of the work order in performance evaluation are effectively ensured.
The technical scheme specifically comprises the following steps:
the work order scoring method is used for intelligently scoring work order feedback data of staff, wherein the work order feedback data comprise work order feedback sentences, and comprises a training process of a learning model, and specifically comprises the following steps:
step A1, presetting a learning set containing preset sentences, and setting a label value for each preset sentence;
step A2, word segmentation processing is carried out on the preset sentences to obtain a plurality of first words contained in the preset sentences;
step A3, vectorizing each first word to generate a corresponding first processing result;
step A4, taking each first processing result and a label value of a preset sentence where a first word corresponding to the first processing result is located as an input value, inputting the input value into a model established according to a preset algorithm for machine learning, and generating a learning model;
the worksheet scoring method further comprises a process of scoring the worksheet feedback data, and specifically comprises the following steps:
step B1, word segmentation processing is carried out on the work order feedback statement to obtain a plurality of second words included in the work order feedback statement;
step B2, vectorizing each second word to generate a corresponding second processing result;
step B3, inputting each second processing result into the learning model to obtain the label value corresponding to the work order feedback statement;
and B4, generating a first score corresponding to the work order feedback statement according to the label value, and then obtaining a final score of each employee according to the first score and outputting the final score.
Preferably, each work order feedback statement corresponds to a work order identifier for distinguishing different work orders, and in step B4, the process of obtaining the score of each employee according to the first score includes:
step B41, obtaining the number of worksheets in the worksheet feedback data according to the worksheet identification;
step B42, generating a second score according to the work order quantity;
step B43, generating a first normalized score corresponding to each employee according to the first scores of all employees;
generating a second normalized score corresponding to each employee according to the second scores of all employees;
and step B44, carrying out weighted summation on the first normalization score and the second normalization score to obtain and output the final score of the employee.
Preferably, in the step B43, the first normalized score corresponding to each employee is generated according to a maximum-minimum normalization algorithm.
Preferably, in the step B43, the second normalized score corresponding to each employee is generated according to a maximum-minimum normalization algorithm.
Preferably, the step S8 further includes: and ranking the staff according to the final score, and outputting a ranking result.
Preferably, the preset algorithm is any one of a naive bayes algorithm, a random forest algorithm and a deep learning algorithm.
Preferably, the first processing result is a mathematical matrix.
Preferably, the second processing result is a mathematical matrix.
Preferably, the step A1 further includes: preprocessing the preset sentences to remove the modifier words and the words without practical significance.
The beneficial effects of the technical scheme are that: the method comprises the steps of firstly, automatically generating a corresponding learning model by a program according to a preset algorithm by means of a mode of inputting a learning set in advance, then carrying out semantic recognition on feedback sentences in feedback data input subsequently by using the learning model, and carrying out intelligent scoring according to recognition results, thereby effectively solving the problems of low accuracy and low recognition rate caused by searching sentence semantics through preset keywords in the traditional programming, solving the problem that the traditional programming method is difficult to effectively understand the meaning of variable sentences input manually, greatly improving the accuracy of analyzing the feedback sentences in the work list by using the program, and effectively ensuring the objectivity and effectiveness of scoring the service quality of the work list in performance assessment.
Drawings
FIG. 1 is a schematic flow chart of learning model training in a worksheet scoring method according to a preferred embodiment of the present invention;
FIG. 2 is a flow chart of the work order feedback data scoring method according to the preferred embodiment of the present invention;
FIG. 3 is a flow chart showing the steps of the method for scoring a work order according to the preferred embodiment of the present invention based on FIG. 2.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The invention is further described below with reference to the drawings and specific examples, which are not intended to be limiting.
The work order scoring method is used for intelligently scoring work order feedback data of staff, wherein the work order feedback data comprise work order feedback sentences, and the work order scoring method comprises a training process of a learning model, as shown in fig. 1, and specifically comprises the following steps:
step A1, presetting a learning set containing preset sentences, and setting a label value for each preset sentence;
step A2, word segmentation processing is carried out on the preset sentences to obtain a plurality of first words contained in the preset sentences;
step A3, vectorizing each first word to generate a corresponding first processing result;
step A4, taking each first processing result and a label value of a preset sentence where a first word corresponding to the first processing result is located as an input value, inputting the input value into a model established according to a preset algorithm for machine learning, and generating a learning model;
the worksheet scoring method further includes a process of scoring the worksheet feedback data, as shown in fig. 2, specifically including:
step B1, word segmentation processing is carried out on the work order feedback statement to obtain a plurality of second words included in the work order feedback statement;
step B2, vectorizing each second word to generate a corresponding second processing result;
step B3, inputting each second processing result into the learning model to obtain the label value corresponding to the work order feedback statement;
and B4, generating a first score corresponding to the work order feedback statement according to the label value, and then obtaining a final score of each employee according to the first score and outputting the final score.
In a specific embodiment of the present invention, the feedback data of the employee worksheet refers to a set of feedback sentences of the served users of all worksheets processed by the employee in a predetermined time period under the name of the employee, when the predetermined time period is defined as one month, the generated worksheet score is a monthly worksheet service quality score, when the predetermined time period is defined as one year, the generated worksheet score is a annual worksheet service quality score, and the length of the collection time period of the feedback data under the name of the employee can be manually set according to needs.
In another embodiment of the present invention, the learning set is preset in a program as an object of machine learning, and the direction of machine learning is determined by the action of manually marking a preset sentence in the learning set with a tag value, where the tag value indicates the feedback quality of the served user, that is, the preference of the served user in the work order under the name of the employee, so as to reflect the quality of the service provided by the employee in the process of performing work order maintenance.
In a preferred embodiment of the present invention, each work order feedback statement corresponds to a work order identifier for distinguishing different work orders, as shown in fig. 3, in step B4, a process of obtaining the score of each employee according to the first score processing specifically includes:
step B41, obtaining the number of worksheets in the worksheet feedback data according to the worksheet identification;
step B42, generating a second score according to the work order quantity;
step B43, generating a first normalized score corresponding to each employee according to the first scores of all employees;
generating a second normalized score corresponding to each employee according to the second scores of all employees;
and step B44, carrying out weighted summation on the first normalization score and the second normalization score to obtain and output the final score of the employee.
In a specific embodiment of the invention, when the number of work orders in the work order feedback data is counted, a data counting algorithm in a big data algorithm is adopted, and here, a pandas tool in a python mathematical programming package is utilized, so that the number of work orders in the feedback data under the name of the employee is automatically counted by a program according to the algorithm, when the feedback data acquisition time period is in month units, the number of work orders represents the monthly work order completion amount of the employee, and when the feedback data acquisition time period is in year units, the number of work orders represents the annual work order completion amount of the employee. The weighted score of the employee is finally obtained by configuring a reasonable percentage for the feedback quality score and the finished work quantity score in the feedback data under the name of the employee.
In a preferred embodiment of the present invention, in the step B43, the first normalized score corresponding to each employee is generated according to a maximum-minimum normalization algorithm.
In the preferred embodiment of the present invention, in the step B43, the second normalized score corresponding to each employee is generated according to a maximum-minimum normalization algorithm.
In a preferred embodiment of the present invention, the step S8 further includes: and ranking the staff according to the final score, and outputting a ranking result.
In a specific embodiment of the invention, after the work order processing quality scores and the work order processing quantity scores of all the staff members within a certain time period are calculated by the scoring method, the work order processing quality and the work order processing quantity are respectively processed by a maximum and minimum normalization algorithm, so that the work order processing quality normalization scores and the work order processing quantity normalization scores of all the staff members are all in a range from 0 to 1, and then the performance assessment scores of each staff member and the ranking results among the staff members are finally obtained by configuring reasonable percentages of the processing quality and the processing quantity.
In a preferred embodiment of the present invention, the preset algorithm is any one of a naive bayes algorithm, a random forest algorithm and a deep learning algorithm.
In one embodiment of the present invention, the program may train different learning models according to the three different algorithms in advance, and finally the user selects the learning model trained by using that algorithm to score the quality of service of the work order for the employee.
In a preferred embodiment of the present invention, the first processing result is a mathematical matrix.
In a preferred embodiment of the present invention, the second processing result is a mathematical matrix.
In a preferred embodiment of the present invention, the step A1 further includes: preprocessing the preset sentences to remove the modifier words and the words without practical significance.
In a specific embodiment of the present invention, the operation of preprocessing the preset sentence may be performed simultaneously with the operation of setting the tag value for the preset sentence, or may be performed before or after the tag is set, which is not in sequence.
The beneficial effects of the technical scheme are that: according to the intelligent scoring method, a machine learning and artificial intelligence technology is introduced, namely, a program automatically generates a corresponding learning model according to a preset algorithm in a mode of inputting a learning set in advance, then semantic recognition is carried out on feedback sentences in feedback data input subsequently by using the learning model, and intelligent scoring is carried out according to recognition results, so that the problems of low accuracy and low recognition rate caused by the fact that sentence semantics are searched through preset keywords in traditional programming are effectively solved, the problem that the traditional programming method is difficult to effectively understand the meaning of variable sentences input manually is solved, the accuracy of analyzing the feedback sentences in a work order by using the program is greatly improved, and objectivity and effectiveness of scoring on the service quality of the work order in performance assessment are effectively ensured.
The foregoing description is only illustrative of the preferred embodiments of the present invention and is not to be construed as limiting the scope of the invention, and it will be appreciated by those skilled in the art that equivalent substitutions and obvious variations may be made using the description and illustrations of the present invention, and are intended to be included within the scope of the present invention.
Claims (7)
1. The work order scoring method is used for intelligently scoring work order feedback data of staff, and is characterized in that the work order feedback data comprise work order feedback sentences, and the work order scoring method comprises a training process of a learning model and specifically comprises the following steps:
step A1, presetting a learning set containing preset sentences, and setting a label value for each preset sentence;
step A2, word segmentation processing is carried out on the preset sentences to obtain a plurality of first words contained in the preset sentences;
step A3, vectorizing each first word to generate a corresponding first processing result;
step A4, taking each first processing result and a label value of a preset sentence where a first word corresponding to the first processing result is located as an input value, inputting the input value into a model established according to a preset algorithm for machine learning, and generating a learning model;
the worksheet scoring method further comprises a process of scoring the worksheet feedback data, and specifically comprises the following steps:
step B1, word segmentation processing is carried out on the work order feedback statement to obtain a plurality of second words included in the work order feedback statement;
step B2, vectorizing each second word to generate a corresponding second processing result;
step B3, inputting each second processing result into the learning model to obtain the label value corresponding to the work order feedback statement;
step B4, generating a first score corresponding to the work order feedback statement according to the label value corresponding to the work order feedback statement, and then obtaining a final score of each employee according to the first score and outputting the final score;
the preset algorithm is any one of a naive Bayes algorithm, a random forest algorithm and a deep learning algorithm;
each work order feedback statement corresponds to a work order identifier for distinguishing different work orders, and in the step B4, the process of obtaining the score of each employee according to the first score processing specifically includes:
step B41, obtaining the number of worksheets in the worksheet feedback data according to the worksheet identification;
step B42, generating a second score according to the work order quantity;
step B43, generating a first normalized score corresponding to each employee according to the first scores of all employees;
generating a second normalized score corresponding to each employee according to the second scores of all employees;
and step B44, carrying out weighted summation on the first normalized score and the second normalized score to obtain the final score of the employee and outputting the final score.
2. The job ticket scoring method according to claim 1, wherein in step B43, the first normalized score corresponding to each employee is generated according to a maximum-minimum normalization algorithm.
3. The job ticket scoring method according to claim 1, wherein in step B43, the second normalized score corresponding to each employee is generated according to a maximum-minimum normalization algorithm.
4. The work order scoring method of claim 1, wherein step B44 further comprises: and ranking the staff according to the final score, and outputting a ranking result.
5. The work order scoring method of claim 1, wherein the first processing result is a mathematical matrix.
6. The work order scoring method of claim 1, wherein the second processing result is a mathematical matrix.
7. The work order scoring method of claim 1, wherein said step A1 further comprises: preprocessing the preset sentences to remove the modifier words and the words without practical significance.
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